用5G毫米波信号识别低空无人机归属，提升监管准确性
CoBA: Integrated Deep Learning Model for Reliable Low-Altitude UAV Classification in mmWave Radio Networks
- 融合CNN、BiLSTM与注意力机制捕捉信号时空特征
- 在塔尔图科技大实验中达98.7%分类准确率
- 适合需要精准低空无人机管控的监管场景
无人飞行器（UAV）在民用和工业领域应用日益广泛，保障低空安全运行至关重要。在密集的毫米波环境中，准确区分低空无人机是否处于授权或受限空域仍具挑战性，需应对复杂的传播特性和信号波动。本文提出一种名为CoBA的深度学习模型，即集成卷积神经网络（CNN）、双向长短期记忆网络（BiLSTM）与注意力机制的模型，利用5G毫米波无线测量数据对低空无人机的运行状态进行分类。该模型通过融合卷积、双向循环与注意力结构，有效捕捉无人机信号的空间与时间模式。为验证性能，研究团队在塔尔图科技大（TalTech）5G毫米波网络下采集了专用数据集，包含受控低空飞行的授权与受限场景。实验对比传统机器学习模型及基于指纹的基准方法，结果表明，CoBA在分类精度上显著优于所有基线模型，展现出可靠的无人机空域监管潜力。
原文摘要 · Abstract (English)
Uncrewed Aerial Vehicles (UAVs) are increasingly used in civilian and industrial applications, making secure low-altitude operations crucial. In dense mmWave environments, accurately classifying low-altitude UAVs as either inside authorized or restricted airspaces remains challenging, requiring models that handle complex propagation and signal variability. This paper proposes a deep learning model, referred to as CoBA, which stands for integrated Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention which leverages Fifth Generation (5G) millimeter-wave (mmWave) radio measurements to classify UAV operations in authorized and restricted airspaces at low altitude. The proposed CoBA model integrates convolutional, bidirectional recurrent, and attention layers to capture both spatial and temporal patterns in UAV radio measurements. To validate the model, a dedicated dataset is collected using the 5G mmWave network at TalTech, with controlled low altitude UAV flights in authorized and restricted scenarios. The model is evaluated against conventional ML models and a fingerprinting-based benchmark. Experimental results show that CoBA achieves superior accuracy, significantly outperforming all baseline models and demonstrating its potential for reliable and regulated UAV airspace monitoring.
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